القائمة
Enterprise AI design patterns for production systems - RAG, Multi-Agent, AI Gateway, LLMOps, and more
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| name | AI Architecture Patterns |
| description | Enterprise AI design patterns for production systems - RAG, Multi-Agent, AI Gateway, LLMOps, and more |
| version | 1.0.0 |
| category | technical |
| command | /ai-architecture-patterns |
| triggers | ["ai pattern","architecture pattern","rag pattern","agent pattern","ai gateway"] |
| type | reference |
Provide expert guidance on selecting and implementing enterprise AI architecture patterns for production systems. This skill contains battle-tested patterns from real-world deployments and the AI Architect Academy.
Problem: Multiple AI services with inconsistent interfaces, no centralized security, and limited observability create management complexity and security risks.
Solution: Deploy a centralized AI gateway that provides unified authentication, rate limiting, request/response logging, and model routing for all AI services.
Key Components:
When to Use:
When NOT to Use:
Problem: LLMs hallucinate and lack access to enterprise-specific knowledge, making them unreliable for business-critical applications.
Solution: Implement a RAG pipeline with document ingestion, chunking, embedding, vector storage, retrieval, and augmented generation with source citations.
Key Components:
When to Use:
When NOT to Use:
Implementation Tips:
Problem: Complex tasks require multiple specialized capabilities that exceed what a single LLM prompt can handle reliably.
Solution: Decompose complex workflows into specialized agents with an orchestrator that coordinates task distribution, handoffs, and result aggregation.
Key Components:
When to Use:
When NOT to Use:
Frameworks:
Problem: N agents x M tools = N*M integrations. Each AI agent needs custom code to integrate with each tool.
Solution: Implement MCP (Model Context Protocol) servers that provide standardized interfaces for tools, resources, and prompts.
Key Components:
When to Use:
Implementation:
import { Server } from '@modelcontextprotocol/sdk/server';
const server = new Server({
name: 'my-mcp-server',
version: '1.0.0'
});
server.tool('search', {
description: 'Search documents',
inputSchema: {
type: 'object',
properties: {
query: { type: 'string' }
}
},
handler: async ({ query }) => {
// Implementation
}
});
Problem: LLM applications lack mature DevOps practices, leading to unpredictable quality and difficult rollbacks.
Solution: Implement prompt versioning, automated evaluation, staged deployments, and continuous monitoring.
Key Components:
When to Use:
Evaluation Metrics:
Problem: Many vector database options with different tradeoffs. Wrong choice leads to expensive migrations.
Solution: Structured decision framework evaluating scale, features, operations, and cost.
Selection Matrix:
| Scale | Recommendation |
|---|---|
| <1M vectors | pgvector (simple), Chroma (prototyping) |
| 1-100M vectors | Weaviate, Qdrant (self-hosted) |
| 100M+ vectors | Pinecone, Milvus (managed) |
Key Considerations:
Problem: Scattered AI initiatives across organization lead to duplicated effort, inconsistent quality, and security gaps.
Solution: Establish centralized governance with standardized patterns, reusable components, and shared infrastructure.
Key Components:
Governance Areas:
Problem: AI introduces new security vectors: prompt injection, data leakage, model manipulation.
Solution: Implement AI-specific security controls including guardrails, PII handling, and audit logging.
Key Controls:
Guardrails Implementation:
from guardrails import Guard
guard = Guard.from_pydantic(output_class=SafeResponse)
response = guard(
llm.invoke,
prompt=user_input,
on_fail="reask"
)
START: What type of AI system?
│
├── Document/Knowledge Q&A
│ └── → RAG Production Pattern
│ ├── Need multiple models? → + AI Gateway
│ └── Sensitive data? → + Security & Governance
│
├── Autonomous Agents
│ └── → Multi-Agent Orchestration
│ ├── Many tools? → + MCP Servers
│ └── Production deployment? → + LLMOps
│
├── Enterprise AI Platform
│ └── → AI Gateway + AI CoE Framework
│ ├── Cost concerns? → + Cost Optimization
│ └── Compliance? → + Security & Governance
│
└── Content Generation
└── → AI Gateway + LLMOps
└── Quality critical? → + Evaluation Pipeline
| Use Case | Primary | Secondary | Tertiary |
|---|---|---|---|
| Customer Support Bot | RAG | AI Gateway | Security |
| Code Assistant | Multi-Agent | MCP Servers | LLMOps |
| Document Intelligence | RAG | Vector DB | AI Gateway |
| Enterprise AI Platform | AI Gateway | AI CoE | Security |
| Research Assistant | RAG | Multi-Agent | LLMOps |
mcp-architecture - MCP server developmentclaude-sdk - Agent development with Claudelanggraph-patterns - Graph-based agent workflowsoci-services-expert - Oracle Cloud guidancePart of the AI Architect Academy by FrankX.AI
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